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World J Clin Oncol. Mar 24, 2026; 17(3): 114744
Published online Mar 24, 2026. doi: 10.5306/wjco.v17.i3.114744
Published online Mar 24, 2026. doi: 10.5306/wjco.v17.i3.114744
Deep learning radiomic analysis in the prediction of MYCN status and survival outcome in children with neuroblastoma
Yu-Han Yang, Yuan Li, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Yang YH and Li Y contributed to conceptualization, computed tomography image segmentation and regions of interest delineation, writing - original draft preparation; writing - review and editing; Yang YH contributed to study design and methodology, data collection and curation, model development, and statistical analysis. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.
Institutional review board statement: This study involving human participants was reviewed and approved by the Institutional Review Boards of the participating institutions. All procedures were conducted in accordance with the ethical standards of the institutional and/or national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent statement: The requirement for written informed consent was waived by the institutional review boards of both participating institutions because the study was retrospective, used existing clinical and imaging records, and analyzed de-identified data.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement:
De-identified individual participant data that underlie the results reported in this article are available from the corresponding author upon reasonable request. Data sharing is subject to approval by the relevant institutional review boards and execution of a data-use agreement to ensure protection of patient privacy and compliance with applicable regulations. Due to institutional policies and patient privacy considerations, raw imaging data or any data containing potentially identifying information will not be publicly released.
Corresponding author: Yu-Han Yang, MD, West China Hospital, Sichuan Medical University, No. 17 People’s South Road, Chengdu 610041, Sichuan Province, China. yyh_1023@163.com
Received: September 28, 2025
Revised: October 21, 2025
Accepted: January 28, 2026
Published online: March 24, 2026
Processing time: 177 Days and 19 Hours
Revised: October 21, 2025
Accepted: January 28, 2026
Published online: March 24, 2026
Processing time: 177 Days and 19 Hours
Core Tip
Core Tip: We constructed a deep learning (DL)-based radiomics signature on computed tomography, which had the ability to identify MYCN amplification in neuroblastoma. Integrating the DL-based radiomics signature and clinical predictors, the nomogram model showed improvement in the prediction of MYCN amplification in neuroblastomas. The DL-based radiomics signature was found to be associated with disease-specific events of neuroblastomas significantly after radical resection.